Note finally that while Hopfield networks can be used for recalling or denoising static memories [1]}, [2]}, we have shown that MPNs can be applied to recall spatiotemporal memories or complete spatiotemporal patterns.
Thus, the MPN with asymmetric weights can be regarded as an STDP-consistent and non-equilibrium generalization of the dynamics arising from the symmetric Hopfield network.
Regarding weight asymmetry, we observe from the trained MPN that the learned weights are considerably asymmetric. The asymmetry measure \(|w_{ij} - w_{ji}| /( (|w_{ij}| + |w_{ji}|)/2) = 1.38, 1,53, 1.58\)  for training parameters \(\tau = 0.5, 1, 2\) , respectively.
